Automated and ERP-Based Diagnosis of Attention-Deficit Hyperactivity Disorder in Children

Transcription

1 Orgnal Artcle Automated and ERP-Based Dagnoss of Attenton-Defct Hyperactvty Dsorder n Chldren Abstract Event-related potental (ERP) s one of the most nformatve and dynamc methods of montorng cogntve processes, whch s wdely used n clncal research to deal wth a varety of psychatrc and neurologcal dsorders such as attenton-defct/hyperactvty dsorder (ADHD). In ths study, there were 60 partcpants ncludng 30 patents wth ADHD and 30 subjects as a control group. Ther ERP sgnals were recorded by three electrodes n two modaltes. After a preprocessng step, several features such as band power, fractal dmenson, autoregressve (AR) model coeffcents and wavelet coeffcents were extracted from recorded sgnals. The am of ths study s to acheve a hgh classfcaton rate. The results show that the fractal dmenson wavelet combnaton features provded a good dscrmnatve capablty; t should be noted that ths mprovement was acheved by combnng all sets of features and applyng a feature selecton algorthm, whch resulted n a maxmum accuracy rate of and 95.39% n support vector machne (SVM) and v_svm classfcaton algorthms usng a 10-fold cross-valdaton approach, respectvely. ERP has been wdely used for clncal dagnoss and cogntve processng defcts n chldren wth ADHD. To ncrease the accuracy of the dagnostc process of ADHD, ERP sgnals were recorded to extract some specfc ERP features related to ths dsease for classfyng the two groups. The results show that the Fra-wave characterzaton produced the best average accuracy wth an effcency of 99.43% for v_svm classfer, compared wth 97.65% effcency for the wavelet features and the other features. Hossen R. Jahanshahloo, Mousa Shams, Elham Ghasem 1, Abolfazl Kouh Department of Electrcal Engneerng, Sahand Unversty of Technology, 1 Department of Agrculture Engneerng, Tabrz Unversty, Tabrz, Iran Keywords: Algorthms, attenton, attenton defct dsorder wth hyperactvty chld, cognton, cognton dsorders, control groups, electrodes, evoked potentals, fractals, humans, nervous system dseases, support vector machne Introducton Attenton-defct/hyperactvty dsorder (ADHD) s one of the most common psychatrc dsorders n school-aged chldren, whch s characterzed by a persstent pattern of mpared attenton, mpulsve behavor, and excessve motor hyperactvty. These manfestatons, related to a chld s conduct, drectly affect academc actvtes, famlar dynamcs, and performance n the socal envronment, whch can negatvely nfluence the personalty development of a chld. [1] The clncal dagnoss of ths dsease requres several medcal specalsts as well as hgh-cost expendture, whch makes t dffcult to access ths beneft among the low-ncome populaton. Event-related potentals (ERPs) are defned as changes n the ongong electroencephalogram (EEG) because of a sensory stmulus. [2] Because these potentals Ths s an open access artcle dstrbuted under the terms of the Creatve Commons Attrbuton-NonCommercal-ShareAlke 3.0 Lcense, whch allows others to remx, tweak, and buld upon the work noncommercally, as long as the author s credted and the new creatons are lcensed under the dentcal terms. For reprnts contact: reprnts@medknow.com are physologcally correlated wth neurocogntve functons, they have been wdely used for clncal dagnoses, bran computer nterface, and especally, n nvestgatons of perceptual and cogntve processng defcts n chldren wth ADHD. The most popularly assessed ERP features for nterpretaton of cogntve processes are the areas and the peaks of ERP components, defned by the mean and peak-to-peak voltages, respectvely. [3] The analyses of these parameters are usually performed n the tme doman, whereby ampltudes and latences of promnent peaks n the averaged potentals are measured and correlated to nformaton processng mechansms. [4] Although the quantfcaton of ERP components by areas and peaks s the standard procedure n fundamental ERP research, the conventonal approach has two drawbacks. Frst, ERPs are tme-varyng sgnals, whch reflect the sum of How to cte ths artcle: Jahanshahloo HR, Shams M, Ghasem E, Kouh A. Automated and ERP-based dagnoss of attenton-defct hyperactvty dsorder n chldren. J Med Sgn Sence 2017;7: Address for correspondence: Mr. Hossen Reza Jahanshahloo, Department of Electrcal Engneerng, Sahand Unversty of Technology, Tabrz, Iran. E-mal: h.jahanshahlo518@gmal.co Webste: Journal of Medcal Sgnals & Sensors Publshed by Wolters Kluwer - Medknow

2 underlyng neural events durng stmulus processng, that operate n dfferent tme scales rangng from mllseconds to seconds. Varous procedures such as ERP subtracton or statstcal methods have been employed to separate functonally meanngful events that partly or completely overlap n tme. However, the relable dentfcaton of these components n ERP waveforms stll remans a problem. Second, analyses n the frequency doman have revealed that EEG/ERP components n dfferent bands (delta, theta, alpha, beta, and gamma) are functonally related to nformaton processng. However, the Fourer transform (FT) of ERP lacks the tme localzaton nformaton of transent neural events. Therefore, usng effcent algorthms for sgnal analyss n tme frequency doman s very mportant to extract and relate dstnct functonal components. These lmtatons, as well as the problems related wth tme-nvarant methods, can be solved by usng the wavelet formalsm. The wavelet transform (WT) s a tme frequency representaton that has an optmal resoluton both n the tme and frequency domans and has been successfully appled to the study of EEG ERP sgnals. [5] Although ERP feature extracton from the tme frequency doman based on the dscrete WT (DWT) has been growng ncreasngly popular, ths approach gves better results for pathology detecton purposes, partcularly n ADHD dentfcaton. Fractal dmenson s an mportant parameter that has sgnfcant applcatons n varous felds ncludng sgnal processng. Sgnal analyss s a hgh-level sgnal processng technque for dentfyng the sgnal features such as roughness, smoothness, and soldty. Lkewse, because of lack of a sutable test or bomarker for dagnoss of ADHD n chldren, ther dentfcaton requres a complete evaluaton of chldren s behavor both at home and at school. As a result, ther dagnoss would be somewhat less accurate and tme consumng. By takng nto account the mpacts of ADHD on the future lves of the chldren sufferng from t, there s an absolute need for a bologcal marker that s capable enough to dagnose ADHD n chldren at the begnnng by observng ts symptoms n chldhood. To ntroduce workng bomarkers, some sgnfcant research has been conducted durng the past few decades about EEG sgnals and ERP for dagnoss of ADHD n chldren. The man goal of ths study s to present a supportng dagnostc tool that uses sgnal processng for feature selecton and machne learnng algorthms for dagnoss. Partcularly, for feature selecton, nformaton theoretc method s proposed, whch s based on fractal dmenson Hguch algorthm and wavelet coeffcents measure. In ths method, a maxmal dscrepancy crteron s ntroduced for selectng dstnct (most dstngushng) features of two groups as well as a sem-supervsed formulaton for effcently updatng the tranng set. Furthermore, support vector machne (SVM) classfer s traned and tested for dentfcaton of robust marker of ERP sgnal for accurate dagnoss of ADHD group. The results show that the proposed approach provdes hgher accuracy n the dagnostc process of ADHD n comparson to the few currently avalable methods. The result of the paper s organzed as follows: Materals and Methods secton gves a descrpton of the method and data acquston condton. Feature Extracton secton presents the detals of the features extracton contanng band power, fractal dmenson Hguch algorthm, autoregressve (AR) coeffcents, and wavelet coeffcents. A bref descrpton of SVM and v_svm classfers s presented n Classfers secton. Performance analyss of SVM and v_svm classfers usng a 10-fold cross-valdaton approach on the extracted features s reported n Results secton. Conclusons from ths study are summarzed n Concluson and Dscusson secton. Materals and Methods Subjects The partcpants n the research were chldren belongng to educatonal nsttutons of the metropoltan area of Manzales, aged between 4 and 15 years, and whose medcal dagnoss had been determned by neurophysologcal evaluaton based on the clncal crtera of Dagnostc and Statstcal Manual of Mental Dsorder (DSM-IV). [6] The chldren were tested n a room under the same lghtng and nose condtons, and wth unformty n the followng consderatons: nonabnormaltes n physcal examnaton, normal vsual and hearng ablty, and ntellgence quotent (IQ) greater than 80. Patents wth pharmacologc management (methylphendate, 20 mg) dd not take ther medcne for up to 24 h before the test. In addton, the comorbdtes n the chldren (oppostonal defant dsorder, specfc phoba, and learnng problems) were taken nto account. In ths study, 60 chldren partcpated, whch ncluded 30 chldren wth ADHD and 30 chldren as a control group. Ther ERP sgnals were recorded by three electrodes located n the mdlne of the head (Pz, Cz, and Fz) accordng to nternatonal system n two modaltes, that s, audtory and vsual, at samplng rate of 640 samples per second. Data acquston The examnaton protocol was conducted accordng to the crtera of Oddball paradgm n the audtory and vsual modaltes. The frst test nvolved the emsson of 80 db tone lastng 50 ms, wth a frequency of 1000 Hz for frequent stmulus and 3000 Hz for nfrequent stmulus, whch were appled randomly at every 1.5 s. In the vsual of the test, the chldren were asked to watch a montor placed 1 m away, whch showed an mage wth a consstent pattern (a checkerboard of 16 squares), whch was the frequent stmulus. The rare stmulus was the presentaton of a target n the center of the screen wth the same common pattern n the background; the chld must press a button each Journal of Medcal Sgnals & Sensors Volume 7 Issue 1 January-March

3 tme the unusual stmulus appeared. The experment conssted of 200 stmul, of whch 80% were frequent and 20% were nfrequent stmul. Preprocessng Each of the classfers was traned wth the correspondng dataset. Because of the large set of features obtaned from ERP measurements (values from all three electrodes n all bands), t was a good dea to reduce ths set and feed the classfer wth the most approprate subset of these features. In addton, the complexty and dmensonalty were reduced. For each SVM classfer [Fgure 1], the forward selecton scheme was appled to choose the best attrbutes or features that corresponded to the selected classfer. Forward selecton scheme was an algorthm that teratvely selected subset of features from a set of features, such that the chosen subset of features was most relevant to the dscrmnaton of the data. By applyng ths technque to each classfer that corresponded to a dfferent condton, the features were obtaned, whch represented the most relevant dscrmnaton of the data n the correspondng condton. Feature extracton Durng sgnal acquston, the recordngs were fltered from 0.3 to 100 Hz. Ths was necessary to mprove the sgnal/ nose rato and to adapt them to later stages. ERP data was frst band pass fltered ( Hz). Then bad channels were dentfed and replaced, and an average reference was computed. The sgnals were analyzed for artfact rejecton and power analyss. Blnks and other artfacts were automatcally dentfed wth a threshold of ±100 μv and excluded from analyses. The clean data was then submtted to a fast Fourer transform (FFT) wth a 1-s Hannng wndow and 50% overlap. Next, ndependent component analyss (ICA) was appled to elmnate dependence among the nput sgnals. Later, features were estmated from dataset to form the ntal space of characterstcs. Fnally, tranng of the classfer and assessment of the classfcaton performance were done wth the selected features [Fgure 1]. In ths secton, the extracted features from ERP sgnals are explaned. A group of these features conssted of Audtory test test Source Separaton (ICA) Feature selecton Feature Extracton Classfcaton SVM, v_svm Fgure 1: Block dagram of the overall procedure for features extracton and classfcaton usng raw ERP sgnals fractal-based features, [7-9] AR coeffcents, [10] band power, [11] and wavelet coeffcents. [12] Hguch fractal dmenson If we consder a tme seres as xðþ; 1 xðþ; 2 :::;xn ð Þ, k new subsequences, x k m can be constructed as: x k m ¼ xm ð Þ; xmþ ð kþ; xmþ ð Nm 2kÞ; :::;xmþ k k for m ¼ 1; 2; :::;k, where m ndcates the ndex of frst sample and k ndcates tme nterval or delay between ponts n the k embeddng sequences. Average length for each tme seres x k m s defned as: L m ðþ¼ k ðn 1 Nm Þ ð Þ=k ¼1 jxðmþ kþxmþ ð ð 1ÞkÞjðn 1Þ N m=kk ð1þ where N s the length of data sequence x. Total average length, L(k), for scale k s computed as follows: Lk ðþ¼ k L m ðþ k ð2þ m¼1 where L(k) s proportonal to k D, and D s the fractal dmenson that s defned usng Hguch s method. [8] In fact, Hguch dmenson s tghtly related wth Takens theory, [13] n whch a sgnal s consdered as the output of a complex system. To characterze ths system, sgnal samples were assgned to state varables actvty. Any subsequent actvty n Hguch method showed the actvty of the state varable through tme. Average length of the subsequent actvtes was nterpreted as the average length of the sgnals produced by the state varables. Autoregressve coeffcents The AR models are used to descrbe a tme seres. Ths model estmates each sample as a weghted sum of prevous samples by a recursve lnear flter. The nteger parameter p s called the order of the AR model. The AR model of a sgnal x(t) n dscrete tme t s defned as follows: p xt ðþ¼a xt ð 1ÞþϵðÞ t ð3þ ¼1 where a 1 ; a 2 ;:::;a p are the coeffcents of recursve flter, p s order of the model, and ϵðþs t uncorrelated nose. The Burg method [10] s appled to ft a Pth order AR model to the nput sgnal, x, by mnmzng (least squares) the forward and backward predcton errors whle constranng the AR parameters to satsfy the Levnson Durbn recurson. These coeffcents can model the whole varaton of a sgnal but they are senstve to addtve nose. 28 Journal of Medcal Sgnals & Sensors Volume 7 Issue 1 January-March 2017

4 Band power ERP can reflect the bran actvty n dfferent frequences. [11] The raw ERP s generally descrbed n terms of fve dfferent frequency bands: gamma (>30 Hz), beta (13 30 Hz), alpha (8 12 Hz), theta (4 8 Hz), and delta (<4 Hz). To determne the band power n the mentoned frequency ntervals, raw ERP sgnal was fltered through band pass flters (ellptc order sx) to represent ERP content n the fve successve frequency bands. In the output of each band pass flter, each sample was squared and averaged over several consecutve samples provdng an estmaton of band power n a wndow wth the length of 1 s. Wavelet The DWT s an effcent tool n sgnal representaton. [12] Each mother wavelet can be compressed to provde a hgher resoluton n a dyadc form and also shfted n each resoluton to model dfferent locatons of a sgnal through the tme doman. A wavelet functon n the scale a and tme shft b s expressed as: ψ ða;bþ ðþ¼2 t a = 2ψ 2 a = 2ðt bþ ð4þ where ψðþs t a wavelet functon. Compressed and stretched dervatons of wavelet functon model are the hgh and low frequency components of a sgnal. Hence, by projectng an orgnal sgnal nto dfferent resolutons, detals of the sgnal can be obtaned. [12] By usng DWT, sgnals are decomposed nto two low and hgh bands at each level that are called approxmatons and detals. Approxmatons represent low frequency components of the sgnal whle detals represent hgh frequency components. In ths study, sgnals were decomposed nto subspaces usng the mother wavelet db4. Then energes correspondng to approxmatons and detals were consdered as sgnal features n each tme frame. [14] Feature selecton Feature selecton s a knd of commonly used dmensonalty reducton method, as opposed to feature extracton such as prncple component analyss (PCA), n whch new lowdmensonal embeddng s produced usng the orgnal features. PCA s one of the most popular appearance-based methods used manly for dmensonalty reducton n compresson and recognton problems. It can be used to reduce the dmensonalty of the feature vectors and elmnates all the statstcal covarance n the transformed feature vectors. Classfers SVM After selectng the relevant features for each dataset, the datasets were fed to SVM classfers and the models were bult. For generalzaton of each classfer model, 10-fold cross-valdatons were used. Cross-valdaton s a technque that clarfes how the chosen classfer model wll generalze to an ndependent dataset that s dfferent from the one that has traned the model. It parttons the data nto n complementary subsets and uses n 1 of the subsets for tranng the model, and the remanng set for testng the model. Ths procedure s repeated n tmes so that each of the subsets s used exactly once as a testng set. The results are then averaged over the rounds to get the fnal estmaton. ν-svm SVM s a method for supervsed learnng that s used for classfcaton or regresson analyss. That means, gven an nput data sample, n whch each data pont s marked as belongng to one of the two possble groups or classes, SVM bulds a model that s then used for classfyng new data ponts to one of the two classes. [15] For the ndependent and dentcally dstrbuted tranng data, x ; y R N fþ1; 1g; ¼ 1; 2; :::;l ð5þ and the orgnal optmzaton problem n ν-svm classfcaton algorthm s as follows: mnτðw; ξ;þ ¼ 1 2 w ν þ 1 l ξ y ð6þ ½ðx wþþbš ξ ð7þ 0; ξ 0 ð8þ Applyng Lagrangan optmzaton theory and ncorporatng kernels for dot products leaves the followng dual quadratc optmzaton problem: maxwðþ¼ a 1 2 a a j y y j K x ; x j ð9þ ;j 0 a 1 l a y ¼ 0 a v ð10þ ð11þ ð12þ The parameters n Eqs. (5) (12) are defned as follows: ν s an upper bound on the fracton of margn errors and lower bound on Journal of Medcal Sgnals & Sensors Volume 7 Issue 1 January-March

5 the fracton of support vectors (SVs) and 0 v 1. To understand theroleof, note that for ξ ¼ 0, the constrant of Eq. (7) smply states that the two classes are separated by the margn 2=w and other parameters have the same meanngs as those n the orgnal method. [16] To compute b and, two sets S ± are consdered, of dentcal sze s > 0, contanng SVs 0 < a < 1=l and y ¼ ±, respectvely. Then, as a result of karush Kuhn tucker (KKT) condtons, constrant Eq. (7) becomes an equalty wth ξ ¼ 0. Hence, n terms of kernels, ¼ 1 2s x S þ j b ¼ 1 2s x S þ S j a j y j K x; x j a j y j K x; x j x S j ð13þ! a j y j K x; x j ð14þ the resultng decson functon can be shown to take the followng form: fðþ¼sgn x a y Kðx; x Þþb ð15þ Results The recordng acquston per patent was made by usng three electrodes (Fz, Cz, and Pz), wth two dfferent types of stmulus, audtory and vsual; so, a total of sx recordngs were acqured per patent. To avod correlatons between electrodes, ICA was appled to generate three uncorrelated sources (for each electrode), on each one of the modaltes. Tables 1 4 show classfcaton rate of power band, fractal dmenson, AR coeffcent, wavelet coeffcent features, and fractal wavelet (Fra-wave) n delta, theta, alpha, and beta bands frequency wth SVM and v_svm classfers, respectvely. Ths study presents an mplementaton of ADHD automatc detecton system, focusng on the dscrmnatve capablty analyss of four dfferent sets of the features and Fra-wave to get the best classfcaton performance. Table 1 presents the performance results of SVM and v_svm classfers consderng dfferent features n delta band frequency. It s presented that the Fra-wave characterzaton produced the best mean accuracy of 92.93% utlzng SVM and 96.07% usng v_svm, compared wth other features. As t s demonstrated n Table 1, v_svm accuracy s better than SVM accuracy consderng the whole set of features. The theta band features are classfed usng SVM and v-svm methods and the results are presented n Table 2. On the bass of ths table, the best average accuracy s obtaned for Frawave characterzaton wth an effcency of 92.62% for Table 1: Classfcaton rate n delta band frequency Feature Class type Audtory Power band SVM v_svm Fractal SVM v_svm AR coeffcent SVM v_svm Wa coeffcent SVM v_svm Fra-wave SVM v_svm Table 2: Classfcaton rate n theta band frequency Feature Class type Audtory Power band SVM v_svm Fractal SVM v_svm AR coeffcent SVM v_svm Wa coeffcent SVM v_svm Fra-wave SVM v_svm Table 3: Classfcaton rate n alpha band frequency Feature Class type Audtory Power band SVM v_svm Fractal SVM v_svm AR coeffcent SVM v_svm Wa coeffcent SVM v_svm Fra-wave SVM v_svm v_svm classfer, whereas t s 98.77% for the wavelet feature and other features. Generally, on the bass of the nformaton provded n Tables 1 4, the audtory case has produced a better accuracy compared wth vsual. 30 Journal of Medcal Sgnals & Sensors Volume 7 Issue 1 January-March 2017

6 Table 4: Classfcaton rate n beta frequency Feature Class type Audtory Power band SVM v_svm Fractal SVM v_svm AR coeffcent SVM v_svm Wa coeffcent SVM v_svm Fra-wave SVM v_svm Concluson and Dscusson Many studes have tred to dentfy behavoral dsorders among chldren by usng dfferent dagnostc methods and tools. Some researchers have used multple sources to gather the comprehensve nformaton. [17,18] Some of these sources were questonnares, ntervews wth famles, parents and chld, and clncal observaton. However, they had consdered much nformaton smultaneously, whch took a long tme and also caused dagnostc errors. In some studes, teacher s ratngs were focused, [19] so the rsk of msdagnoss was hgh. In some studes, nformaton obtaned from both parents and teacher s ratngs were used for dagnosng behavoral dsorders; [20] however, usng ths amount of nformaton for dagnoss takes a long tme, wth hgh cost and hgh rsk of errors. ERP and EEG were appled for accurate dagnoss. ERP has been wdely used for clncal dagnoss and cogntve processng defcts n chldren wthadhd.toncreasethe accuracy of dagnoss processes for ADHD, ERP sgnals were recorded to extract some specfc ERP features related to ths dsease and the partcpants were classfed n two groups. In ths way, after preprocessng of sgnals, several features were extracted from the recorded sgnals, some of whch reflected the complexty and roughness of the sgnals (fractal-based features), and the others modeled the sgnals n the tme (AR coeffcents), spectral (band power), and tme frequency domans (wavelet coeffcents). Next, these extracted features were appled to the classfers such as SVM and v_svm. Results of ths experment showed the supremacy of v_svm classfer. Ths study presents an mplementaton of an ADHD automatc detecton system, focused on the dscrmnatve capablty analyss of four dfferent sets of features to get the best classfcaton performance. The example, Table 2 presents the performances results, showng that the Fra-wave characterzaton produced the best average accuracy wth an effcency of 98.77% for v_svm classfer, compared wth the 96.68% effcency for the wavelet features and the other features. However, the combnaton of them produced a better accuracy for v_svm classfer. The fact can also be seen n Tables 1 4 that audtory produced a better accuracy compared wth vsual. The results can be compared wth some prevous research. As the frst study, Jaojao and colleagues used channel 128, K-nearest neghbors (K-NN), and SVM to classfy ADHD. The results showed that the best classfcaton accuracy of 83.33% was acheved by K-NN classfer. [21] Mueller et al. used two groups of age-matched adults (75 ADHD and 75 controls) and performed a vsual two stmulus go/no-go task. ERP responses were decomposed nto ndependent components, and a selected set of ndependent ERP component features were used for SVM classfcaton. Usng a 10-fold cross-valdaton approach, classfcaton accuracy was 91%. Predctve power of SVM classfer was verfed on the bass of the ndependent ADHD sample (17 ADHD patents), resultng n a classfcaton accuracy of 94%. [22] On the other hand, n our research, we have used vsual and audtory stmulus to acheve better classfcaton accuracy. Zeynab et al. have obtaned 215 wavelet coeffcents and 20 recurrence quantfcaton analyss (RQA) features that could dscrmnate 31 ADHD and 37 normal groups sgnfcantly. They have used K-fold (5-fold) cross-valdaton n whch all data have been randomly dvded nto fve groups. Classfcaton results have demonstrated that combnaton of two knds, Morlet and Mexcan hat kernel classfcaton of features, represents the sgnal n a better way. Results show that there s approxmately no dfference between accuracy of Morlet and Mexcan hat kernel classfcaton approaches, and the accuracy of both methods was 98%. [23] Our study can be enhanced by consderng the followng two man factors: usng more sgnal recordng channels and ncreasng the number of partcpants n the test. Ths study s the frst attempt to classfy ADHD patents by means of SVM and ndependent ERP sgnal wth combned feature Fra-wave. The results demonstrate the effcency of the utlzed approach. As a future study, ndependent component decomposton and feature extracton procedures can be consdered. Moreover, ths promsng approach can easly be appled to other clncal problems. Acknowledgements We thank Dr. P. Castro-Cabrera Unversty of Unversdad Natonal de Colomba, sede Manzales for provdng the ERP dataset. Fnancal support and sponsorshp Nl. Conflcts of nterest There are no conflcts of nterest. Journal of Medcal Sgnals & Sensors Volume 7 Issue 1 January-March

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